Compartmentalized mathematical model to predict future number of active cases and deaths of COVID-19
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ORIGINAL ARTICLE
Compartmentalized mathematical model to predict future number of active cases and deaths of COVID-19 Osmar Pinto Neto 1,2,3 & José Clark Reis 2 & Ana Carolina Brisola Brizzi 1,2 & Gustavo José Zambrano 2 & Joabe Marcos de Souza 2,4 & Wellington Pedroso 1,2 & Rodrigo Cunha de Mello Pedreiro 1,5,6 & Bruno de Matos Brizzi 2 & Ellysson Oliveira Abinader 7 & Renato Amaro Zângaro 1,3 Received: 21 April 2020 / Accepted: 20 August 2020 # Sociedade Brasileira de Engenharia Biomedica 2020
Abstract Introduction In December 2019, China reported a series of atypical pneumonia cases caused by a new Coronavirus, called COVID-19. In response to the rapid global dissemination of the virus, on the 11th of Mars, the World Health Organization (WHO) has declared the outbreak a pandemic. Considering this situation, this paper intends to analyze and improve the current SEIR models to better represent the behavior of the COVID-19 and accurately predict the outcome of the pandemic in each social, economic, and political scenario. Methodology We present a generalized Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model and test it using a global optimization algorithm with data collected from the WHO. Results The main results were: (a) Our model was able to accurately fit the either deaths or active cases data of all tested countries using optimized coefficient values in agreement with recent reports; (b) when trying to fit both sets of data at the same time, fit was good for most countries, but not all. (c) Using our model, large ranges for each input, and optimization we predict death values for 15, 30, 45, and 60 days ahead with errors in the order of 5, 10, 20, and 80%, respectively; (d) sudden changes in the number of active cases cannot be predicted by the model unless data from outside sources are used. Conclusion The results suggest that the presented model may be used to predict 15 days ahead values of total deaths with errors in the order of 5%. These errors may be minimized if social distance data are inputted into the model. Keywords COVID-19 . Compartmental model . Active cases . Deaths . Epidemiological model predictions
Introduction * Osmar Pinto Neto [email protected] 1
Biomedical Engineering Department, Anhembi Morumbi University, Sao Paulo, SP, Brazil
2
Arena235 Research Lab – São José dos Campos, Sao Jose dos Campos, SP, Brazil
3
Center for Innovation, Technology and Education – CITE, Parque Tecnológico de São José dos Campos, Estrada Dr. Altino Bondensan, 500, Sao Jose dos Campos, SP 12247-016, Brazil
4
Departamento de Engenharia Aeronáutica, Universidade de São Paulo, Sao Paulo, SP, Brazil
5
Estácio de Sá University, Nova Fribugo, RJ, Brazil
6
Santo Antônio de Pádua College, Santo Antonio de Padua, RJ, Brazil
7
Instituto Abinader, Manaus, AM, Brazil
In December 2019, in China, a series of atypical pneumonia cases have emerged caused by a new Coronavirus, nowadays officially called COVID-19 by the World Health Organization (WHO). It has spread rapidly throughout the
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